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Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.
Explores spatial regression models, addressing spatial autocorrelation challenges and the concept of spatial lag models to correct biases and improve inference accuracy.
Explores multilinear regression for design optimization and orthogonality, covering teamwork, abstracts, linear and quadratic models, ANOVA, and alias structures.
